Can I use these patterns for production applications?

Yes, this skill is specifically designed to help developers move from experimental, 'vibes-based' prompting to reliable, production-grade AI interactions.

What is few-shot learning in this context?

Few-shot learning involves providing the model with a small set of high-quality examples (input-output pairs) within the prompt to guide its behavior through pattern matching.

What are prompt engineering patterns?

They are standardized, repeatable techniques like few-shot learning and chain-of-thought used to improve the accuracy, reasoning, and consistency of LLM responses.

How does this skill improve Claude's output?

It applies structured reasoning frameworks and systematic optimization workflows to reduce hallucinations and ensure the model follows complex constraints and instructions.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: HermeticOrmus

byHermeticOrmus

•

数据科学与机器学习

Optimizes LLM performance and reliability through advanced prompting techniques like chain-of-thought and few-shot learning.

关于

This skill provides a comprehensive framework for designing, testing, and refining prompts to ensure production-grade LLM outputs. It equips developers with standardized patterns for structured reasoning, dynamic example selection, and template-based interpolation, enabling the creation of highly controllable and consistent AI-driven applications. By implementing these battle-tested patterns, developers can significantly reduce hallucinations, improve instruction-following, and maximize the utility of Large Language Models within their software ecosystem.

主要功能

Modular template systems with variable interpolation
Advanced system prompt design for precise behavioral control
2 GitHub stars
Few-shot learning with dynamic example selection strategies
Chain-of-thought reasoning and self-consistency patterns
Systematic prompt optimization and iterative A/B testing

使用场景

Building production-ready LLM applications with high consistency requirements
Refining complex system instructions for specialized AI agents and assistants
Implementing structured output formats and error recovery in AI workflows

关于

主要功能

Modular template systems with variable interpolation
Advanced system prompt design for precise behavioral control
2 GitHub stars
Few-shot learning with dynamic example selection strategies
Chain-of-thought reasoning and self-consistency patterns
Systematic prompt optimization and iterative A/B testing

使用场景

Building production-ready LLM applications with high consistency requirements
Refining complex system instructions for specialized AI agents and assistants
Implementing structured output formats and error recovery in AI workflows